Statistical pattern recognition (SPR) has been established as the state of the art for making data-driven predictions in the context of physical-asset management technologies such as structural health monitoring (SHM) systems [1]} and digital twins [2]}, [3]}. Statistical classifiers are a fundamental component of the SPR approach to these decision-support technologies – enabling the categorisation of acquired data into groups, or classes. For example, in the context of SHM, by associating the target classes of a classifier with salient health-states (e.g. undamaged, nascent damage, severe damage), inferences can be made regarding the condition of a structure of interest. Recent research has focussed on utilising probabilistic predictions from statistical classifiers in risk-based approaches for supporting the operation and maintenance (O&M) decision processes engineers are tasked with when managing structural assets [4]}, [5]}, [6]}. Defined as a product of probability and cost, risk is akin to negative expected utility. Decisions can then be made by selecting strategies that maximise expected utility, or minimise risk.
